Multiple-Parameter Joint Estimation for Unmanned Mining Trucks Based on Real-Time Cornering Stiffness Identification

High-precision estimation of key vehicle–road state parameters is essential for accurate and safe vehicle control, as well as for reliable trajectory tracking and path planning. However, the strong nonlinearity of tire forces makes real-time estimation of tire cornering stiffness challenging, and the uncertainty of this parameter significantly limits the performance of conventional estimation methods. To overcome this issue, this study proposes a novel joint estimation framework that integrates the Square-Root Cubature Kalman Filter (SCKF) with Moving Horizon Estimation (MHE). In the proposed approach, the SCKF provides high-accuracy estimation of the vehicle sideslip angle, while the MHE enables real-time identification and updating of tire cornering stiffness using vehicle state information over a short horizon. This establishes a closed-loop joint estimation mechanism between sideslip angle and cornering stiffness. Furthermore, the real-time updated cornering stiffness is incorporated into the estimation of the road adhesion coefficient, thereby improving its accuracy. The framework is validated through co-simulation between Carsim and Simulink, as well as experimental testing. Both results confirm its superior estimation accuracy and robustness, demonstrating its potential to enhance vehicle control systems.